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.github/workflows/arm-ci-extended-cpp.yml
if [[ $running_containers == "" ]]; then echo "No running containers"; else echo "Running container(s) found" && \ docker stop $running_containers; fi docker container prune -f docker image prune -af - name: Clean repository shell: bash
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.github/workflows/arm-cd.yml
if [[ $running_containers == "" ]]; then echo "No running containers"; else echo "Running container(s) found" && \ docker stop $running_containers; fi docker container prune -f - name: Clean repository shell: bash
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tensorflow/c/eager/c_api_unified_experimental.cc
limitations under the License. ==============================================================================*/ #include "tensorflow/c/eager/c_api_unified_experimental.h" #include <vector> #include "absl/container/flat_hash_map.h" #include "absl/strings/str_cat.h" #include "tensorflow/c/eager/c_api_unified_experimental_internal.h" #include "tensorflow/c/tf_datatype.h" #include "tensorflow/c/tf_status.h"
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ci/official/utilities/cleanup_docker.sh
# ============================================================================== cat <<EOF IMPORTANT: These tests ran under docker. This script does not clean up the container for you! You can delete the container with: $ docker rm -f tf You can also execute more commands within the container with e.g.: $ docker exec tf bazel clean $ docker exec -it tf bash EOF
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# different compilation methods. E.g. for a PR to test a new CUDA version, set # the CACHEBUSTER to the PR number. build --action_env=CACHEBUSTER=20220325 # Use Python 3.X as installed in container image build --action_env PYTHON_BIN_PATH="/usr/local/bin/python3" build --python_path="/usr/local/bin/python3" # Build TensorFlow v2 build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
# targets matching the provided query, which start with // or @ but not # //tensorflow (so it looks for //third_party, //external, etc.), and then # gathers the list of all packages (i.e. directories) which contain those # targets. license_query() { bazel cquery --experimental_cc_shared_library "$1" --keep_going \ | grep -e "^//" -e "^@" \ | grep -E -v "^//tensorflow" \ | sed -e 's|:.*||' \ | sort -u }
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tensorflow/c/c_api.cc
const char** containers, int ncontainers, TF_Status* status) { std::vector<string> container_names(ncontainers); for (int i = 0; i < ncontainers; ++i) { container_names[i] = containers[i]; } status->status = Reset(opt->options, container_names); } extern "C" { void TF_Reset(const TF_SessionOptions* opt, const char** containers,
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ci/official/containers/linux_arm64/setup.python.sh
# Usage: setup.python.sh <pyversion> <requirements.txt> set -xe source ~/.bashrc VERSION=$1 REQUIREMENTS=$2 add-apt-repository ppa:deadsnakes/ppa # Install Python packages for this container's version cat >pythons.txt <<EOF $VERSION $VERSION-dev $VERSION-venv $VERSION-distutils EOF /setup.packages.sh pythons.txt # Re-link pyconfig.h from aarch64-linux-gnu into the devtoolset directory
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CONTRIBUTING.md
development to avoid installing the packages directly on your system (in which case remember to change the directory from `/root` to `/tensorflow` once you get into the running container so `bazel` can find the `tensorflow` workspace). you can do this by using the following command. As an example- ```bash docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10
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tensorflow/c/eager/c_api_experimental.h
// which is passed to the functions referenced in the TFE_CustomDevice struct // `device` (execute, delete_device, etc.). It can for example contain the // names of wrapped devices. // // There are currently no graph semantics implemented for registered custom // devices, so executing tf.functions which contain operations placed on the // custom devices will fail. // // `device_name` must not name an existing physical or custom device. It must
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